无需参考图像，自动优化锥形束CT重建参数，提升图像质量。
No-reference based automatic parameter optimization for iterative reconstruction using a novel search space aware crow search algorithm
- 用改进的 crow search 算法搜索最优重建参数，兼顾局部与全局搜索。
- 在多个数据集上平均性能提升4.19%，细节更清晰。
- 适合需要自动化、无参考图像的医学影像重建场景。
迭代重建技术可通过减少投影数量降低辐射暴露，受到广泛关注。然而，该方法通常需精确调节多个超参数，对重建质量影响显著。手动调参耗时且增加人工负担。本文提出一种全新的全自动参数优化框架，适用于多种锥形束计算机断层扫描（CBCT）迭代重建算法，在无需参考重建图像的情况下确定最优参数。所提方法融合改进的麻雀搜索算法（CSA），具备依赖集合的局部搜索机制、搜索空间感知的全局搜索策略，以及目标驱动的局部与全局搜索平衡。为确保有效初始种群，还提出混沌对角线线性均匀初始化方案，加速算法收敛。在三台成像设备和四个真实数据集上评估，涵盖三种具有最多可调参数的迭代重建方法，代表最复杂场景。结果表明，该方法优于人工设置和标准CSA，平均适应度提升4.19%，在CHILL@UK和RPI_AXIS两个无参考学习质量指标上分别提升4.89%和3.82%。定性结果显示，所提方法能更好保持精细结构。整体表现证明其在各类场景下的有效性与鲁棒性。
原文摘要 · Abstract (English)
Iterative reconstruction technique's ability to reduce radiation exposure by using fewer projections has attracted significant attention. However, these methods typically require a precise tuning of several hyperparameters, which can have a major impact on reconstruction quality. Manually setting these parameters is time-consuming and increases the workload for human operators. In this paper, we introduce a novel fully automatic parameter optimization framework that can be applied to a wide range of Cone-beam computed tomography (CBCT) iterative reconstruction algorithms to determine optimal parameters without requiring a reference reconstruction. The proposed method incorporates a modified crow search algorithm (CSA) featuring a superior set-dependent local search mechanism, a search-space-aware global search strategy, and an objective-driven balance between local and global search. Additionally, to ensure an effective initial population, we propose a chaotic diagonal linear uniform initialization scheme that accelerates algorithm convergence. The performance of the proposed framework was evaluated on three imaging machines and four real datasets, as well as three different iterative reconstruction methods with the highest number of tunable parameters, representing the most challenging senario. The results indicate that the proposed method could outperform manual settings and CSA, with an 4.19% improvement in average fitness and 4.89% and 3.82% improvements on CHILL@UK and RPI_AXIS, respectively, which are two benchmark no-reference learning-based quality metrics. In addition, the qualitative results clearly show the superiority of the proposed method by maintaining fine details sharply. The overall performance of the proposed framework across different comparison scenarios demonstrates its effectiveness and robustness across all cases.
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